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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 351–356Cite as

A Game-Engine-Based Learning Environment Framework for Artificial General Intelligence

Toward Democratic AGI

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

Abstract

Artificial General Intelligence (AGI) refers to machine intelligence that can effectively conduct variety of human tasks. Therefore AGI research requires multivariate and realistic learning environments. In recent years, game engines capable of constructing highly realistic 3D virtual worlds have also become available at low cost. In accordance with these changes, we developed the “Life in Silico” (LIS) framework, which provides virtual agents with learning algorithms and their learning environments with game engine. This should in turn allow for easier and more flexible AGI research. Furthermore, non-experts will be able to play with the framework, which would enable them to research as their hobby. If AGI research becomes popular in this manner, we may see a sudden acceleration towards the “Democratization of AGI”.

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Notes

  1. 1.

    http://opencog.org/.

  2. 2.

    https://github.com/OC2MC/opencog-to-minecraft.

  3. 3.

    http://wiki.opencog.org/w/Setting_up_the_Unity3D_world.

  4. 4.

    https://gym.openai.com/.

  5. 5.

    https://github.com/wbap/lis.

References

  1. Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T.P., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning (2016). arXiv preprint arXiv:1602.01783

  2. Lerer, A., Gross, S., Fergus, R.: Learning physical intuition of block towers by example (2016). arXiv preprint arXiv:1603.01312

  3. Abel, D., et al.: Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains (2016). arXiv preprint arXiv:1603.01312

  4. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)

    Google Scholar 

  5. Mnih, V., et al.: Humanlevel control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)

    Article  Google Scholar 

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Acknowledgements

Thanks to all members of the WBAI and the members of the WBA Future Leaders.

Author information

Authors and Affiliations

  1. Dwango Artificial Intelligence Laboratory, DWANGO Co., Ltd., Kabukiza Tower 14F, 4-12-15 Ginza, Chuo-Ku, Tokyo, 104-0061, Japan

    Masayoshi Nakamura & Hiroshi Yamakawa

  2. The Whole Brain Architecture Initiative, Kabukiza Tower 14F, 4-12-15 Ginza, Chuo-Ku, Tokyo, 104-0061, Japan

    Masayoshi Nakamura & Hiroshi Yamakawa

Authors
  1. Masayoshi Nakamura
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  2. Hiroshi Yamakawa
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Corresponding author

Correspondence to Masayoshi Nakamura .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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© 2016 Springer International Publishing AG

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Cite this paper

Nakamura, M., Yamakawa, H. (2016). A Game-Engine-Based Learning Environment Framework for Artificial General Intelligence. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_39

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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